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7.3 Paper 3: Conditional Variational Autoencoder for Prediction and Feature Recovery Applied to Intrusion Detection in IoT Manuel Lopez-Martin, Belen Carro, Antonio Sanchez-Esguevillas and Jaime Lloret. Conditional Variational Autoencoder for Prediction and Feature Recovery Applied to Intrusion Detection in IoT Sensors 2017, 17(9), 1967 2.677 Q1 12 (https://scholar.google.es/citations?user=3RSZbOYAAAAJ&hl=es) Published, August-2017 https://doi.org/10.3390/s17091967 7.3.1 Objectives This paper has two main objectives: (1) to apply a C-VAE to the intrusion detection problem in data networks, and (2) to be able to synthesize predictors (features) with the same probability distribution as the originals, making it possible to apply this capability to recover damaged datasets or with missing features. In the first objective the intention is to obtain better prediction metrics than with classic algorithms (Random Forest, SVM, Logistic Regression...). In the second objective the purpose is to achieve an accuracy of the synthetic features as high as possible. 7.3.2 Datasets For this work we have used the NSL-KDD [67] dataset. This is a classic Intrusion Detection dataset. The dataset has 32 continuous and 3 categorical features, with an intrusion label of 5 values (Normal, DoS, Probe, R2L and U2R). This is a quite unbalanced dataset which is important to be representative of the datasets found with intrusion detection problems. The dataset needed to be transformed before applying the detection algorithm. All categorical variables were one-hot encoded and the continuous were scaled in the range [0,1]. The dataset was split between training and test subsets, with 125973 and 22544 samples respectively. Authors Journal Journal Impact Factor Quartile #Citations Status Link Doctoral Thesis: Novel applications of Machine Learning to NTAP - 71PDF Image | Novel applications of Machine Learning to Network Traffic Analysis
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